基于深度卷积神经网络的认证公钥圆曲线用于网络安全图像加密应用程序.
Esam A A Hagras1, Saad Aldosary2, Haitham Khaled3
1Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种新的网络安全图像加密方法,使用验证的公钥圆曲线深卷积神经网络 (APK-EC-DCNN). 该技术通过先进的加密和混乱映射方法来增强数据隐私和安全性.
科学领域:
- 计算机科学 计算机科学
- 密码学 密码学 密码学 密码学
- 图像处理 图像处理
背景情况:
- 对强大的网络安全解决方案的需求不断增长,以保护信息流和数据隐私.
- 需要先进的加密技术,能够处理像图像这样的复杂数据.
研究的目的:
- 为网络安全图像加密提出一种新的认证公钥圆曲线深卷积神经网络 (APK-EC-DCNN).
- 增强数据的真实性,保密性和对抗攻击的稳定性.
主要方法:
- 使用圆曲线的Diffie-Hellman密钥交换 (EC-DHKE) 进行安全的会话密钥生成.
- 采用3D量子混沌物流地图 (3D QCLM) 来实现高级安全和混乱行为.
- 集成一个安全的哈希函数与DCNN和3D QCLM输出用于认证扩展扩散矩阵 (AEDM).
- 应用部分频域加密 (PFDE) 使用离散波形变换来提高稳定性和速度.
主要成果:
- 拟议的APK-EC-DCNN加密算法在图像质量和安全性方面表现出高性能.
- 该加密方法显示出对噪声和信号处理攻击的显著稳定性.
- 与现有的加密技术相比,实现了最先进的性能.
结论:
- 开发的APK-EC-DCNN方法为网络安全应用中的图像加密提供了安全有效的解决方案.
- 圆曲线加密,深度学习和混乱地图的结合提供了一个强大的防御机制.
- 该算法成功地平衡了安全性,质量和稳定性,以实现实际实施.
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